发表机构
University of Southern California; KDDI Research, Inc.（南加州大学; KDDI研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对城市轨迹生成的隐私与多分辨率模式问题，提出MR-Traj多分辨率扩散框架，建模粗粒度里程碑与细粒度片段，在全局分布相当的同时提升细粒度性能并降低轨迹链接风险。
AI 中文摘要
理解人类流动性对交通管理、疫情防控、城市规划等广泛城市应用至关重要。但受隐私问题影响，大规模公开轨迹数据的可用性有限，给下游流动性分析带来挑战。现有合成轨迹生成方法主要关注全局分布相似度匹配，却常忽略对实际应用至关重要的不同时空分辨率下的流动性模式。为应对这些挑战，我们提出一种新型多分辨率扩散框架MR-Traj，用于大规模轨迹生成。MR-Traj明确将轨迹建模为粗粒度里程碑与细粒度片段的组合，可捕捉多分辨率下的复杂时空依赖关系。实验结果表明，MR-Traj在全局分布相似度方面达到与现有最优方法相当的性能，在细粒度流动性模式建模及支持下游城市流动性任务方面则始终优于现有方法。此外，通过在多个分辨率层级引入随机性，MR-Traj生成更多样化的轨迹，在种子引导的数据发布设置下，可经验性降低轨迹链接风险。
英文摘要
Understanding human mobility is critical for a wide range of urban applications, including traffic management, epidemic control, and urban planning. However, due to privacy concerns, the availability of large-scale public trajectory data remains limited, posing challenges for downstream mobility analysis. Existing methods for synthetic trajectory generation primarily focus on matching global distribution similarity, while often overlooking mobility patterns across different spatial and temporal resolutions that are essential for practical utility. To address these challenges, we propose a novel multi-resolution diffusion framework, MR-Traj, for large-scale trajectory generation. MR-Traj explicitly models trajectories as compositions of coarse-grained milestones and fine-grained segments, enabling the capture of complex spatial-temporal dependencies at multiple resolutions. Experimental results demonstrate that MR-Traj achieves comparable performance to state-of-the-art methods in terms of global distribution similarity, while consistently outperforming them in modeling fine-resolution mobility patterns and supporting downstream urban mobility tasks. In addition, by introducing stochasticity at multiple resolution levels, MR-Traj generates more diverse trajectories, which empirically reduces trajectory linkage risk under a seed-guided data release setting. Our code is available at https://github.com/Ray0202/MR-Traj.
Comments12 pages, 4 figures. Accepted to KDD 2026
